Statistical machine learning merges statistics with the computational sciences---computer science, systems science and optimization. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring statistical methodology to bear. Fields such as bioinformatics, artificial intelligence, signal processing, communications, networking, information management, finance, game theory and control theory are all being heavily influenced by developments in statistical machine learning.
The field of statistical machine learning also poses some of the most challenging theoretical problems in modern statistics, chief among them being the general problem of understanding the link between inference and computation.
Research in statistical machine learning at Berkeley builds on Berkeley's world-class strengths in probability, mathematical statistics, computer science and systems science. Moreover, by its interdisciplinary nature, statistical machine learning helps to forge new links among these fields.
An education in statistical machine learning at Berkeley thus involves an immersion in the traditions of statistical science broadly defined, a thoroughgoing involvement in exciting applied problems, and an opportunity to help shape the future of statistics.